Pat Pannuto

Today's computational devices are overwhelmingly wireless. To realize
wireless communication, today’s devices use a grab bag of protocols
(Bluetooth, WiFi, 4G/5G, LoRa, NFC, etc.) and no one universal standard
has emerged. This diversity presents a ripe ped- agogical opportunity to
introduce students to the fundamental tradeoffs and design decisions
inherent to wireless communication and networking. Furthermore, many
wireless protocols are accessi- ble to study in a classroom (in fact,
many we all use daily), which lends to a very hands-on course.

We report on our experience teaching a new ``Wireless Networking for the
Internet of Things'' course in three R1 universities across six offerings,
with sections both in quarter and semester format and for undergraduate,
graduate, and professional-master's levels. We share the scope of the
covered topics, our approach for making the course interactive and
hands-on, lessons learned from multiple iterations, adaptations to fit
within different prerequisite chains, and different structures to adapt
to different delivery formats.

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Today’s computational devices are overwhelmingly wireless. To realize wireless communication, today’s devices use a grab bag of protocols (Bluetooth, WiFi, 4G/5G, LoRa, NFC, etc.) and no one universal standard has emerged. This diversity presents a ripe ped- agogical opportunity to introduce students to the fundamental tradeoffs and design decisions inherent to wireless communication and networking. Furthermore, many wireless protocols are accessi- ble to study in a classroom (in fact, many we all use daily), which lends to a very hands-on course.

We report on our experience teaching a new “Wireless Networking for the Internet of Things” course in three R1 universities across six offerings, with sections both in quarter and semester format and for undergraduate, graduate, and professional-master’s levels. We share the scope of the covered topics, our approach for making the course interactive and hands-on, lessons learned from multiple iterations, adaptations to fit within different prerequisite chains, and different structures to adapt to different delivery formats.

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1.5 billion smartphones are sold annually, and most are decommissioned less than two years later. Most of these unwanted smartphones are neither discarded nor recycled but languish in junk drawers and storage units. This computational stockpile represents a substantial wasted potential: modern smartphones have increasingly high-performance and energy-efficient processors, extensive networking capabilities, and a reliable built-in power supply. This project studies the ability to reuse smartphones as "junkyard computers." Junkyard computers grow global computing capacity by extending device lifetimes, which supplants the manufacture of new devices. We show that the capabilities of even decade-old smartphones are within those demanded by modern cloud microservices and discuss how to combine phones to perform increasingly complex tasks. We describe how current operation-focused metrics do not capture the actual carbon costs of compute. We propose Computational Carbon Intensity---a performance metric that balances the continued service of older devices with the superlinear runtime improvements of newer machines. We use this metric to redefine device service lifetime in terms of carbon efficiency. We develop a cloudlet of reused Pixel 3A phones. We analyze the carbon benefits of deploying large, end-to-end microservice-based applications on these smartphones. Finally, we describe system architectures and associated challenges to scale to cloudlets with hundreds and thousands of smartphones.

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1.5 billion smartphones are sold annually, and most are decommissioned less than two years later. Most of these unwanted smartphones are neither discarded nor recycled but languish in junk drawers and storage units. This computational stockpile represents a substantial wasted potential: modern smartphones have increasingly high-performance and energy-efficient processors, extensive networking capabilities, and a reliable built-in power supply. This project studies the ability to reuse smartphones as "junkyard computers." Junkyard computers grow global computing capacity by extending device lifetimes, which supplants the manufacture of new devices. We show that the capabilities of even decade-old smartphones are within those demanded by modern cloud microservices and discuss how to combine phones to perform increasingly complex tasks. We describe how current operation-focused metrics do not capture the actual carbon costs of compute. We propose Computational Carbon Intensity—a performance metric that balances the continued service of older devices with the superlinear runtime improvements of newer machines. We use this metric to redefine device service lifetime in terms of carbon efficiency. We develop a cloudlet of reused Pixel 3A phones. We analyze the carbon benefits of deploying large, end-to-end microservice-based applications on these smartphones. Finally, we describe system architectures and associated challenges to scale to cloudlets with hundreds and thousands of smartphones.

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EffiSenseSee is a means to identify energy-inefficient lighting infrastructure lingering in our built environment.
When light bulbs convert incoming AC power into visible light, a carefully designed camera can ``see'' some of the original AC signal in the varying light intensity.
Using a large corpus of over 60 bulbs, we show that based solely on analysis of subtleties in observed light output, previously unseen bulbs can be classified as energy-efficient or energy-inefficient with over 95\% accuracy.
With 89\% accuracy, EffiSenseSee can group bulbs into incandescent, halogen, CFL, or LED—the four primary categories measured by regulatory bodies.
We then investigate how to bring this technology ``out of the lab.''
In an outdoor setting with a commodity camera, EffiSenseSee identifies inefficient bulbs with 74\% accuracy.

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EffiSenseSee is a means to identify energy-inefficient lighting infrastructure lingering in our built environment. When light bulbs convert incoming AC power into visible light, a carefully designed camera can “see” some of the original AC signal in the varying light intensity. Using a large corpus of over 60 bulbs, we show that based solely on analysis of subtleties in observed light output, previously unseen bulbs can be classified as energy-efficient or energy-inefficient with over 95% accuracy. With 89% accuracy, EffiSenseSee can group bulbs into incandescent, halogen, CFL, or LED—the four primary categories measured by regulatory bodies. We then investigate how to bring this technology “out of the lab.” In an outdoor setting with a commodity camera, EffiSenseSee identifies inefficient bulbs with 74% accuracy.

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This paper discusses experiments on soil-based microbial fuel cells (MFCs) as energy scavenging sources.
We explain the mechanism of operation for MFCs, perform controlled laboratory experiments of MFCs, and deploy a small-scale in-situ pilot in an active farm.
We find that traditional energy harvester ICs draw power too aggressively, which reduces overall energy capture.
We show that isolated MFCs can be combined in series or parallel to improve the voltage or current output of the harvesting source.
Lastly, we observe that under a real-world, drip-irrigated agricultural setting, MFC output is appreciably lower, but consistent at 0.5-2 microwatts.

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This paper discusses experiments on soil-based microbial fuel cells (MFCs) as energy scavenging sources. We explain the mechanism of operation for MFCs, perform controlled laboratory experiments of MFCs, and deploy a small-scale in-situ pilot in an active farm. We find that traditional energy harvester ICs draw power too aggressively, which reduces overall energy capture. We show that isolated MFCs can be combined in series or parallel to improve the voltage or current output of the harvesting source. Lastly, we observe that under a real-world, drip-irrigated agricultural setting, MFC output is appreciably lower, but consistent at 0.5-2 microwatts.

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In this paper, we provide the first broad measurement study of the operation, adoption, performance, and efficacy of Helium.
The Helium network aims to provide low-power, wide-area network wireless
coverage for Internet of Things-class devices.
In contrast to traditional infrastructure, ``hotspots'' (base stations) are owned and
operated by individuals who are paid by the network for
providing coverage and are paid directly by users for ferrying data.

As of May, 2021, Helium has over 40,000 active hotspots with 1,000 new hotspots coming online every day.
This deployment is decentralized -- 84\% of users own at most three hotspots.
Some support infrastructure remains highly centralized, however, with over 99\% of data traffic routed through one cloud endpoint and multiple cities in which all hotspots rely on one ISP for backhaul.
Helium is largely speculative today with more hotspot activity than user activity.
Crowdsourced, incentive-guided infrastructure deployment largely works but shows evidence of gamification and apathy.
As Helium lacks clear, radio-oriented coverage maps, we develop and test coverage models based on network incentives.
Finally, empirical testing with IoT devices finds basic success, but uncovers numerous reliability issues.

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In this paper, we provide the first broad measurement study of the operation, adoption, performance, and efficacy of Helium. The Helium network aims to provide low-power, wide-area network wireless coverage for Internet of Things-class devices. In contrast to traditional infrastructure, “hotspots” (base stations) are owned and operated by individuals who are paid by the network for providing coverage and are paid directly by users for ferrying data.

As of May, 2021, Helium has over 40,000 active hotspots with 1,000 new hotspots coming online every day. This deployment is decentralized – 84% of users own at most three hotspots. Some support infrastructure remains highly centralized, however, with over 99% of data traffic routed through one cloud endpoint and multiple cities in which all hotspots rely on one ISP for backhaul. Helium is largely speculative today with more hotspot activity than user activity. Crowdsourced, incentive-guided infrastructure deployment largely works but shows evidence of gamification and apathy. As Helium lacks clear, radio-oriented coverage maps, we develop and test coverage models based on network incentives. Finally, empirical testing with IoT devices finds basic success, but uncovers numerous reliability issues.

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Generally, infrastructure is susceptible to, and therefore must
constantly combat, corrosion.
The monitoring and maintenance of
corrosion protection (or the consequences of its unchecked failure) is often
one of the leading costs of infrastructure upkeep.
Galvanic cathodic protection is a common corrosion control technique
that is employed in applications from home appliances to boats to bridges.
At its core, however, galvanic cathodic protection is simply an
electrochemical cell---that is, a battery.
This presents an opportunity to treat this corrosion protection as an
in-situ power source that \emph{by definition} will last as long as the
protection system itself.
In this paper, we explore the efficacy of these pervasive, ``ambient
galvanic cells'' as potential energy harvesting sources.
We then show how to use these cells as a power source for wireless sensing
devices that monitor the health of the same corrosion protection system.
Our system takes advantage of newly available LPWAN technologies that allow
for effortless wide-area coverage.
We demonstrate the viability and efficacy of the system on one of the most
common galvanic cathodic protection systems, home hot water heaters.
We show that this technique can be a powerful new asset for corrosion
monitoring and for deploying wireless sensor networks broadly.

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Generally, infrastructure is susceptible to, and therefore must constantly combat, corrosion. The monitoring and maintenance of corrosion protection (or the consequences of its unchecked failure) is often one of the leading costs of infrastructure upkeep. Galvanic cathodic protection is a common corrosion control technique that is employed in applications from home appliances to boats to bridges. At its core, however, galvanic cathodic protection is simply an electrochemical cell—that is, a battery. This presents an opportunity to treat this corrosion protection as an in-situ power source that by definition will last as long as the protection system itself. In this paper, we explore the efficacy of these pervasive, “ambient galvanic cells” as potential energy harvesting sources. We then show how to use these cells as a power source for wireless sensing devices that monitor the health of the same corrosion protection system. Our system takes advantage of newly available LPWAN technologies that allow for effortless wide-area coverage. We demonstrate the viability and efficacy of the system on one of the most common galvanic cathodic protection systems, home hot water heaters. We show that this technique can be a powerful new asset for corrosion monitoring and for deploying wireless sensor networks broadly.

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Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity---they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.

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Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity—they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.

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The vision of sensor systems that collect critical and previously ungathered
information about the world is often only realized when sensors, students,
and subjects move outside the academic laboratory. However, deployments at
even the smallest scales introduce complexities and risks that can be
difficult for a research team to anticipate. Over the past year, our
interdisciplinary team of engineers and economists has been designing,
deploying, and operating a large sensor network in Accra, Ghana that
measures power outages and quality at households and firms. This network
consists of 457 custom sensors, over 3,000 mobile app instances,
thousands of participant surveys, and custom user incentive and
deployment management systems. In part, this deployment supports an
evaluation of the impacts of investments in the grid on reliability and
the subsequent effects of improvements in reliability on
socioeconomic well-being. We report our experiences as we move from
performing small pilot deployments to our current scale, attempting to
identify the pain points at each stage of the deployment. Finally, we
extract high-level observations and lessons learned from our
deployment activities, which we wish we had originally known when
forecasting budgets, human resources, and project timelines. These
insights will be critical as we look toward scaling our deployment to the
en-tire city of Accra and beyond, and we hope that they will encourage and
support other researchers looking to measure highly granular information
about our world’s critical systems.

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The vision of sensor systems that collect critical and previously ungathered information about the world is often only realized when sensors, students, and subjects move outside the academic laboratory. However, deployments at even the smallest scales introduce complexities and risks that can be difficult for a research team to anticipate. Over the past year, our interdisciplinary team of engineers and economists has been designing, deploying, and operating a large sensor network in Accra, Ghana that measures power outages and quality at households and firms. This network consists of 457 custom sensors, over 3,000 mobile app instances, thousands of participant surveys, and custom user incentive and deployment management systems. In part, this deployment supports an evaluation of the impacts of investments in the grid on reliability and the subsequent effects of improvements in reliability on socioeconomic well-being. We report our experiences as we move from performing small pilot deployments to our current scale, attempting to identify the pain points at each stage of the deployment. Finally, we extract high-level observations and lessons learned from our deployment activities, which we wish we had originally known when forecasting budgets, human resources, and project timelines. These insights will be critical as we look toward scaling our deployment to the en-tire city of Accra and beyond, and we hope that they will encourage and support other researchers looking to measure highly granular information about our world’s critical systems.

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The Internt of Things (IoT) is a rapidly evolving application space. One
of the fascinating new fields in IoT research is mm-scale sensors, which
make up a myriad of new application domains. Enabled by the unique
characteristics of cyber-physical systems and recent advances in low-power
design and bare-die 3D chip stacking, mm-scale sensors are rapidly becoming
a reality. In this paper, we will survey the challenges and solutions to
3D-stacked mm-scale design, highlighting low-power circuit issues ranging
from low-power SRAM and miniature neural network accelerators to radio
communication protocols and analog interfaces. We will discuss system-level
challenges and illustrate several complete systems and their merging
applicaiton spaces.

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The Internt of Things (IoT) is a rapidly evolving application space. One of the fascinating new fields in IoT research is mm-scale sensors, which make up a myriad of new application domains. Enabled by the unique characteristics of cyber-physical systems and recent advances in low-power design and bare-die 3D chip stacking, mm-scale sensors are rapidly becoming a reality. In this paper, we will survey the challenges and solutions to 3D-stacked mm-scale design, highlighting low-power circuit issues ranging from low-power SRAM and miniature neural network accelerators to radio communication protocols and analog interfaces. We will discuss system-level challenges and illustrate several complete systems and their merging applicaiton spaces.

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Incentives are a key facet of human studies research, yet the state-of-the-art
often designs and implements incentive systems in an ad-hoc, on-demand manner.
We introduce the first vocabulary for formally describing incentive
systems and develop a software infrastructure that enables UI-based graphical
generation of complex, auditable, reliable, and reproducible incentive systems.
We call this infrastructure the Open INcentive Kit (OINK).
A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate
incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

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Incentives are a key facet of human studies research, yet the state-of-the-art often designs and implements incentive systems in an ad-hoc, on-demand manner. We introduce the first vocabulary for formally describing incentive systems and develop a software infrastructure that enables UI-based graphical generation of complex, auditable, reliable, and reproducible incentive systems. We call this infrastructure the Open INcentive Kit (OINK). A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

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Smart and connected devices offer enormous potential to enable
context-aware, localized, and multi-device orchestrations that could
substantially increase the reach and utility of computing.  The growth
of these applications has been hampered, however, as devices, their
data, and their control have been largely sequestered to their own
vendor-specific APIs, clouds, and applications---a largely stove-piped
state of affairs.  In instances where barriers between devices have
been pierced, the connections often occur between vendor clouds,
affecting the latency, privacy, and reliability of the original
application, while simultaneously making them more complex.  Locally
executing applications have not materialized as devices with
incompatible communication protocols, inconsistent APIs, and
incongruent data models rarely communicate. We claim that what is
needed to unlock the application potential is an architecture tailored
to facilitating applications composed of networked devices.

Our proposed architecture addresses this by providing a port-based abstraction
for devices using a small wrapper layer. This device abstraction
provides a consistent view of devices, and embeddable runtimes provide existing
applications straightforward access to devices. The architecture also supports
device discovery, shared interfaces between devices, and an application
specification interface that promotes creating device-agnostic applications
capable of operating even when devices change.  We demonstrate the efficacy of
our architecture with two application case studies that highlight the
abstraction layers between applications and devices and employ the
embeddability of our system to add new functionality to existing systems.

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Smart and connected devices offer enormous potential to enable context-aware, localized, and multi-device orchestrations that could substantially increase the reach and utility of computing. The growth of these applications has been hampered, however, as devices, their data, and their control have been largely sequestered to their own vendor-specific APIs, clouds, and applications—a largely stove-piped state of affairs. In instances where barriers between devices have been pierced, the connections often occur between vendor clouds, affecting the latency, privacy, and reliability of the original application, while simultaneously making them more complex. Locally executing applications have not materialized as devices with incompatible communication protocols, inconsistent APIs, and incongruent data models rarely communicate. We claim that what is needed to unlock the application potential is an architecture tailored to facilitating applications composed of networked devices.

Our proposed architecture addresses this by providing a port-based abstraction for devices using a small wrapper layer. This device abstraction provides a consistent view of devices, and embeddable runtimes provide existing applications straightforward access to devices. The architecture also supports device discovery, shared interfaces between devices, and an application specification interface that promotes creating device-agnostic applications capable of operating even when devices change. We demonstrate the efficacy of our architecture with two application case studies that highlight the abstraction layers between applications and devices and employ the embeddability of our system to add new functionality to existing systems.

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Network connectivity is often one of the most challenging aspects of deploying
sensors. In many countries, cellular networks provide the most reliable,
highest bandwidth, and greatest coverage option for internet access. While this makes smartphones a seemingly ideal platform to serve as a gateway between sensors and the cloud,
we find that a device designed for
multi-tenant operation and frequent human interaction becomes unreliable
when tasked to continuously run a single application with no human
interaction, a seemingly counter-intuitive result. Further, we find that economy phones cannot physically
withstand continuous operation, resulting in a surprisingly high rate of
permanent device failures in the field. If these observations hold more broadly, they would make mobile phones
poorly suited to a range of sensing applications for which they have been 
rumored to hold great promise.

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Network connectivity is often one of the most challenging aspects of deploying sensors. In many countries, cellular networks provide the most reliable, highest bandwidth, and greatest coverage option for internet access. While this makes smartphones a seemingly ideal platform to serve as a gateway between sensors and the cloud, we find that a device designed for multi-tenant operation and frequent human interaction becomes unreliable when tasked to continuously run a single application with no human interaction, a seemingly counter-intuitive result. Further, we find that economy phones cannot physically withstand continuous operation, resulting in a surprisingly high rate of permanent device failures in the field. If these observations hold more broadly, they would make mobile phones poorly suited to a range of sensing applications for which they have been rumored to hold great promise.

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Ultra wideband technology has shown great promise for providing
high-quality location estimation, even in complex indoor multipath
environments, but existing ultra wideband systems require tens to hundreds of
milliwatts during operation.
Backscatter communication has demonstrated the viability of astonishingly
low-power tags, but has thus far been restricted to
narrowband systems with low localization resolution.
The challenge to combining these complimentary technologies is that they
share a compounding limitation, constrained transmit power. Regulations limit
ultra wideband transmissions to just -41.3\,dBm/MHz, and a backscatter device
can only reflect the power it receives.
The solution is long-term integration of this limited power, lifting the
initially imperceptible signal out of the noise.
This integration only works while the target is stationary. However, stationary
describes the vast majority of objects, especially lost ones.
With this insight, we design Slocalization, a sub-microwatt,
decimeter-accurate localization system that opens a new tradeoff space in
localization systems and realizes an energy, size, and cost point that invites
the localization of every thing.
To evaluate this concept,
we implement an energy-harvesting Slocalization tag and find that
Slocalization can recover ultra wideband backscatter
in under fifteen minutes across thirty meters of space
and localize tags with a mean 3D Euclidean error of only 30\,cm.

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Ultra wideband technology has shown great promise for providing high-quality location estimation, even in complex indoor multipath environments, but existing ultra wideband systems require tens to hundreds of milliwatts during operation. Backscatter communication has demonstrated the viability of astonishingly low-power tags, but has thus far been restricted to narrowband systems with low localization resolution. The challenge to combining these complimentary technologies is that they share a compounding limitation, constrained transmit power. Regulations limit ultra wideband transmissions to just -41.3 dBm/MHz, and a backscatter device can only reflect the power it receives. The solution is long-term integration of this limited power, lifting the initially imperceptible signal out of the noise. This integration only works while the target is stationary. However, stationary describes the vast majority of objects, especially lost ones. With this insight, we design Slocalization, a sub-microwatt, decimeter-accurate localization system that opens a new tradeoff space in localization systems and realizes an energy, size, and cost point that invites the localization of every thing. To evaluate this concept, we implement an energy-harvesting Slocalization tag and find that Slocalization can recover ultra wideband backscatter in under fifteen minutes across thirty meters of space and localize tags with a mean 3D Euclidean error of only 30 cm.

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City-scale sensing holds the promise of enabling a deeper understanding of our
urban environments.
However, a city-scale deployment requires physical installation,
power management, and communications---all challenging tasks standing between a
good idea and a realized one.
This indicates the need for a platform that enables easy deployment and
experimentation for applications operating at city scale.
To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing.
Signpost simplifies deployment by eliminating the need
for connection to wired infrastructure and instead harvesting energy from an
integrated solar panel. The platform furnishes the key resources necessary to 
support multiple, pluggable sensor modules while providing
fair, safe, and reliable sharing in the face of dynamic energy constraints.
We deploy Signpost with several sensor modules, showing the viability
of an energy-harvesting, multi-tenant, sensing system, and evaluate its
ability to support sensing applications.
We believe Signpost reduces the difficulty inherent in city-scale deployments,
enables new experimentation, and provides improved insights into urban health.

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City-scale sensing holds the promise of enabling a deeper understanding of our urban environments. However, a city-scale deployment requires physical installation, power management, and communications—all challenging tasks standing between a good idea and a realized one. This indicates the need for a platform that enables easy deployment and experimentation for applications operating at city scale. To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing. Signpost simplifies deployment by eliminating the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel. The platform furnishes the key resources necessary to support multiple, pluggable sensor modules while providing fair, safe, and reliable sharing in the face of dynamic energy constraints. We deploy Signpost with several sensor modules, showing the viability of an energy-harvesting, multi-tenant, sensing system, and evaluate its ability to support sensing applications. We believe Signpost reduces the difficulty inherent in city-scale deployments, enables new experimentation, and provides improved insights into urban health.

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Low-power microcontrollers lack some of the hardware features and
memory resources that enable multiprogrammable systems.  Accordingly,
microcontroller-based operating systems have not provided important
features like fault isolation, dynamic memory allocation, and
flexible concurrency. However, an emerging class of embedded
applications are software platforms, rather than single purpose
devices, and need these multiprogramming features.  Tock, a new
operating system for low-power platforms, takes advantage of limited
hardware-protection mechanisms as well as the type-safety features of the
Rust programming language to provide a multiprogramming environment
for microcontrollers.  Tock isolates software faults, provides
memory protection, and efficiently manages memory for dynamic
application workloads written in any language. It achieves this while
retaining the dependability requirements of long-running applications.

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Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.

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We present SurePoint, a system for drop-in, high-fidelity indoor localization.
SurePoint builds on recently available commercial ultra-wideband radio hardware.
While ultra-wideband radio hardware can provide the timing primitives necessary
for a simple adaptation of two-way ranging, we show that with the addition of
frequency and spatial diversity, we can achieve a 53\% decrease in median
ranging error.
Because this extra diversity requires many additional packets for each range
estimate, we next develop an efficient broadcast ranging protocol for
localization that ameliorates this overhead.
We evaluate the performance of this ranging protocol in stationary and
fast-moving environments and find that it achieves up to 0.08\,m median error
and 0.53\,m 99th percentile error.
As ranging requires the tag to have exclusive access to the channel, we next
develop a protocol to coordinate the localization of multiple tags in space.
This protocol builds on recent work exploiting the constructive interference
phenomenon. The ultra-wideband PHY uses a different modulation scheme
compared to the narrowband PHY used by previous work, thus we first explore the
viability and performance of constructive interference with ultra-wideband
radios.
Finally, as the ranging protocol requires careful management of the
ultra-wideband radio and tight timing, we develop TriPoint, a dedicated
``drop-in'' ranging module that provides a simple \iic interface. We show that
this additional microcontroller demands only marginal energy overhead while
facilitating interoperability by freeing the primary microcontroller to
handle other tasks.

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We present SurePoint, a system for drop-in, high-fidelity indoor localization. SurePoint builds on recently available commercial ultra-wideband radio hardware. While ultra-wideband radio hardware can provide the timing primitives necessary for a simple adaptation of two-way ranging, we show that with the addition of frequency and spatial diversity, we can achieve a 53% decrease in median ranging error. Because this extra diversity requires many additional packets for each range estimate, we next develop an efficient broadcast ranging protocol for localization that ameliorates this overhead. We evaluate the performance of this ranging protocol in stationary and fast-moving environments and find that it achieves up to 0.08 m median error and 0.53 m 99th percentile error. As ranging requires the tag to have exclusive access to the channel, we next develop a protocol to coordinate the localization of multiple tags in space. This protocol builds on recent work exploiting the constructive interference phenomenon. The ultra-wideband PHY uses a different modulation scheme compared to the narrowband PHY used by previous work, thus we first explore the viability and performance of constructive interference with ultra-wideband radios. Finally, as the ranging protocol requires careful management of the ultra-wideband radio and tight timing, we develop TriPoint, a dedicated “drop-in” ranging module that provides a simple I²C interface. We show that this additional microcontroller demands only marginal energy overhead while facilitating interoperability by freeing the primary microcontroller to handle other tasks.

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We introduce \emph{Harmonium}, a novel ultra-wideband RF localization architecture
that achieves decimeter-scale accuracy indoors.
Harmonium strikes a balance between tag simplicity and processing complexity to
provide fast and accurate indoor location estimates.
Harmonium uses only commodity components and consists of a small, inexpensive,
lightweight, and FCC-compliant ultra-wideband transmitter or \emph{tag}, fixed
infrastructure \emph{anchors} with known locations, and centralized processing
that calculates the tag's position.
Anchors employ a new frequency-stepped narrowband receiver architecture that
rejects narrowband interferers and
extracts high-resolution timing information without the cost or complexity of
traditional ultra-wideband approaches.
In a complex indoor environment, 90\% of position estimates obtained with
Harmonium exhibit less than 31\,cm of error with an average 9\,cm of inter-sample
noise. In non-line-of-sight conditions (i.e. through-wall), 90\% of position error is less than
42\,cm.
The tag draws 75\,mW when actively transmitting, or 3.9\,mJ per location fix
at the 19\,Hz update rate.  Tags weigh 3\,g and cost \$4.50\,USD at modest
volumes.
Harmonium introduces a new design point for indoor localization
and enables localization of small, fast objects such as micro quadrotors,
devices previously restricted to expensive optical motion capture
systems.

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We introduce Harmonium, a novel ultra-wideband RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive, lightweight, and FCC-compliant ultra-wideband transmitter or tag, fixed infrastructure anchors with known locations, and centralized processing that calculates the tag’s position. Anchors employ a new frequency-stepped narrowband receiver architecture that rejects narrowband interferers and extracts high-resolution timing information without the cost or complexity of traditional ultra-wideband approaches. In a complex indoor environment, 90% of position estimates obtained with Harmonium exhibit less than 31 cm of error with an average 9 cm of inter-sample noise. In non-line-of-sight conditions (i.e. through-wall), 90% of position error is less than 42 cm. The tag draws 75 mW when actively transmitting, or 3.9 mJ per location fix at the 19 Hz update rate. Tags weigh 3 g and cost $4.50 USD at modest volumes. Harmonium introduces a new design point for indoor localization and enables localization of small, fast objects such as micro quadrotors, devices previously restricted to expensive optical motion capture systems.

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As we show in this paper, I/O has become the limiting
factor in scaling down size and power toward the goal of
invisible computing. Achieving this goal will require
composing optimized and specialized---yet
reusable---components with an interconnect that permits
tiny, ultra-low power systems.  In contrast to today's
interconnects which are limited by power-hungry
pull-ups or high-overhead chip-select lines, our
approach provides a superset of common bus features but
at lower power, with fixed area and pin count, using
fully synthesizable logic, and with surprisingly low
protocol overhead.

We present \textbf{MBus}, a new 4-pin,
22.6\,pJ/bit/chip chip-to-chip interconnect made of two
``shoot-through'' rings.  MBus facilitates ultra-low
power system operation by implementing automatic
power-gating of each chip in the system, easing the
integration of active, inactive, and activating
circuits on a single die.  In addition, we introduce a
new bus primitive: power oblivious communication, which
guarantees message reception regardless of the
recipient's power state when a message is sent.  This
disentangles power management from communication,
greatly simplifying the creation of viable, modular,
and heterogeneous systems that operate on the order of
nanowatts.

To evaluate the viability, power, performance,
overhead, and scalability of our design, we build both
hardware and software implementations of MBus and show
its seamless operation across two FPGAs and twelve
custom chips from three different semiconductor
processes.  A three-chip, 2.2\,mm$^3$ MBus system draws
8\,nW of total system standby power and uses only
22.6\,pJ/bit/chip for communication.  This is the
lowest power for any system bus with MBus's feature
set.

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As we show in this paper, I/O has become the limiting factor in scaling down size and power toward the goal of invisible computing. Achieving this goal will require composing optimized and specialized—yet reusable—components with an interconnect that permits tiny, ultra-low power systems. In contrast to today’s interconnects which are limited by power-hungry pull-ups or high-overhead chip-select lines, our approach provides a superset of common bus features but at lower power, with fixed area and pin count, using fully synthesizable logic, and with surprisingly low protocol overhead.

We present MBus, a new 4-pin, 22.6 pJ/bit/chip chip-to-chip interconnect made of two “shoot-through” rings. MBus facilitates ultra-low power system operation by implementing automatic power-gating of each chip in the system, easing the integration of active, inactive, and activating circuits on a single die. In addition, we introduce a new bus primitive: power oblivious communication, which guarantees message reception regardless of the recipient’s power state when a message is sent. This disentangles power management from communication, greatly simplifying the creation of viable, modular, and heterogeneous systems that operate on the order of nanowatts.

To evaluate the viability, power, performance, overhead, and scalability of our design, we build both hardware and software implementations of MBus and show its seamless operation across two FPGAs and twelve custom chips from three different semiconductor processes. A three-chip, 2.2 mm³ MBus system draws 8 nW of total system standby power and uses only 22.6 pJ/bit/chip for communication. This is the lowest power for any system bus with MBus’s feature set.

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Currently, researchers study face-to-face interactions using wearable sensors and
smartphones which provide 2 to 5~m proximity sensing every 20 to 300~s.
However, studying interaction distance, which is known to
impact disease spread, communication behavior, and other phenomenon, has
proven challenging.
Smartphones are limited by their inaccurate and/or
impractical ranging capabilities, while wearable sensors
are limited by their need for
infrastructure nodes, bulkiness, and/or inaccurate ranging.
To address these challenges, we present Opo, a 14~cm$^2$, 11.4~g ``lapel pin''
built from commercial components.
Opo sensors range neighbors every 2~s up to 2~m away with 5\% average
error, all while requiring zero
infrastructure and improving upon current wearable sensors' accuracy and power usage.
The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19~\uA
when no neighbors are present. This enables Opo sensors to discover and range
neighbors without the need for infrastructure nodes and slow or power-hungry
RF discovery protocols.
Thus, Opo is able to sense interaction distance with high accuracy (5~cm)
and temporal fidelity (2~s) on a limited power budget.

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Currently, researchers study face-to-face interactions using wearable sensors and smartphones which provide 2 to 5 m proximity sensing every 20 to 300 s. However, studying interaction distance, which is known to impact disease spread, communication behavior, and other phenomenon, has proven challenging. Smartphones are limited by their inaccurate and/or impractical ranging capabilities, while wearable sensors are limited by their need for infrastructure nodes, bulkiness, and/or inaccurate ranging. To address these challenges, we present Opo, a 14 cm², 11.4 g “lapel pin” built from commercial components. Opo sensors range neighbors every 2 s up to 2 m away with 5% average error, all while requiring zero infrastructure and improving upon current wearable sensors’ accuracy and power usage. The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19 μA when no neighbors are present. This enables Opo sensors to discover and range neighbors without the need for infrastructure nodes and slow or power-hungry RF discovery protocols. Thus, Opo is able to sense interaction distance with high accuracy (5 cm) and temporal fidelity (2 s) on a limited power budget.

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We propose an ultra-low power interconnect bus for
millimeter-scale wireless sensor nodes. Using only 4~IO pads,
the bus minimizes the required chip real estate, enabling
ultra-small form factors in modular sensor node
designs. Low power is achieved using a ``clockless''
design of member nodes while aggressive power gating
allows an ultra-low power standby mode with only
53~gates powered on. An integrated wakeup scheme is
compatible with PMUs that have a special low power
standby mode. The MBus is fully synthesizable and uses
robust timing. Implemented in a 3~module system in
180~nm technology, Mbus achieves 8~nW of standby power
and 17.5~pJ/bit/chip.

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We propose an ultra-low power interconnect bus for millimeter-scale wireless sensor nodes. Using only 4 IO pads, the bus minimizes the required chip real estate, enabling ultra-small form factors in modular sensor node designs. Low power is achieved using a “clockless” design of member nodes while aggressive power gating allows an ultra-low power standby mode with only 53 gates powered on. An integrated wakeup scheme is compatible with PMUs that have a special low power standby mode. The MBus is fully synthesizable and uses robust timing. Implemented in a 3 module system in 180 nm technology, Mbus achieves 8 nW of standby power and 17.5 pJ/bit/chip.

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We explore the indoor positioning problem with unmodified smartphones
and slightly-modified commercial LED luminaires.  The
luminaires---modified to allow rapid, on-off keying---transmit their
identifiers and/or locations encoded in human-imperceptible optical
pulses.  A camera-equipped smartphone, using just a single image frame
capture, can detect the presence of the luminaires in the image,
decode their transmitted identifiers and/or locations, and determine
the smartphone's location and orientation relative to the luminaires.
Continuous image capture and processing enables continuous position
updates.
The key insights underlying this work are (i) the driver circuits of
emerging LED lighting systems can be easily modified to transmit data
through on-off keying; (ii) the rolling shutter effect of CMOS imagers
can be leveraged to receive many bits of data encoded in the optical
transmissions with just a single frame capture, (iii) a camera is
intrinsically an angle-of-arrival sensor, so the projection of
multiple nearby light sources with known positions onto a camera's
image plane can be framed as an instance of a sufficiently-constrained
angle-of-arrival localization problem, and (iv) this problem can be
solved with optimization techniques.  We explore the feasibility of
the design through an analytical model, demonstrate the viability of
the design through a prototype system, discuss the challenges to a
practical deployment including usability and scalability, and
demonstrate decimeter-level accuracy in both carefully controlled and
more realistic human mobility scenarios.

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We explore the indoor positioning problem with unmodified smartphones and slightly-modified commercial LED luminaires. The luminaires—modified to allow rapid, on-off keying—transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone’s location and orientation relative to the luminaires. Continuous image capture and processing enables continuous position updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame capture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera’s image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.

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The Internet of Things (IoT) is a rapidly emerging
application space, poised to become the largest
electronics market for the semiconductor industry. IoT
devices are focused on sensing and actuating of our
physical environment and have a nearly unlimited
breadth of uses. In this paper, we explore the IoT
application space and then identify two common
challenges that exist across this space: ultra-low
power operation and system design using modular,
composable components. We survey recent low power
techniques and discuss a low power bus that enables
modular design. Finally, we conclude with three example
ultra-low power, millimeter-scale IoT systems.

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The Internet of Things (IoT) is a rapidly emerging application space, poised to become the largest electronics market for the semiconductor industry. IoT devices are focused on sensing and actuating of our physical environment and have a nearly unlimited breadth of uses. In this paper, we explore the IoT application space and then identify two common challenges that exist across this space: ultra-low power operation and system design using modular, composable components. We survey recent low power techniques and discuss a low power bus that enables modular design. Finally, we conclude with three example ultra-low power, millimeter-scale IoT systems.

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We present a $2\times4\times4$~mm$^3$ imaging system
complete with optics, wireless communication, battery, power
management, solar harvesting, processor and memory. The system
features a 160$\times$160 resolution CMOS image sensor with 304nW
continuous in-pixel motion detection mode.  System components
are fabricated in five different IC layers and die-stacked for
minimal form factor.  Photovoltaic (PV) cells face the opposite
direction of the imager for optimal illumination and generate
456~nW at 10~klux to enable energy autonomous system operation.

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We present a 2×4×4 mm³ imaging system complete with optics, wireless communication, battery, power management, solar harvesting, processor and memory. The system features a 160×160 resolution CMOS image sensor with 304nW continuous in-pixel motion detection mode. System components are fabricated in five different IC layers and die-stacked for minimal form factor. Photovoltaic (PV) cells face the opposite direction of the imager for optimal illumination and generate 456 nW at 10 klux to enable energy autonomous system operation.

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Most modern software-defined radios are large, expensive, and
power-hungry, and this diminishes their utility in low-power,
size-constrained settings like sensor networks and mobile computing.
We explore the viability of scaling down the software radio in size,
cost, and power, and show that an index card-sized, sub-\$150, `AA'
battery-powered system is possible using off-the-shelf components.
Key to our approach is that we leverage an integrated, reconfigurable,
flash-based FPGA with a hard ARM Cortex-M3 microprocessor which
simultaneously enables lower power and tighter hardware/soft\-ware
integration than prior designs.  This architecture allows us to
implement timing-critical MAC protocols and validate the speculated
performance of several recent MAC/PHY primitives and protocols
including Backcast, A-MAC, and Glossy using an IEEE 802.15.4-compliant
radio implementation that interoperates with commercial radios.  The
work also identifies several enhancements in the underlying hardware
components that could improve power, performance, and flexibility.

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Most modern software-defined radios are large, expensive, and power-hungry, and this diminishes their utility in low-power, size-constrained settings like sensor networks and mobile computing. We explore the viability of scaling down the software radio in size, cost, and power, and show that an index card-sized, sub-$150, ‘AA’ battery-powered system is possible using off-the-shelf components. Key to our approach is that we leverage an integrated, reconfigurable, flash-based FPGA with a hard ARM Cortex-M3 microprocessor which simultaneously enables lower power and tighter hardware/soft-ware integration than prior designs. This architecture allows us to implement timing-critical MAC protocols and validate the speculated performance of several recent MAC/PHY primitives and protocols including Backcast, A-MAC, and Glossy using an IEEE 802.15.4-compliant radio implementation that interoperates with commercial radios. The work also identifies several enhancements in the underlying hardware components that could improve power, performance, and flexibility.

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